Design and implement data-driven risk scoring and prioritization approaches across SaaS security signals.
Lead the development of AI-powered product capabilities, including agentic and LLM-based features that support investigation, triage, and security operations workflows.
Define and evolve explainable decision logic so customers understand why issues are prioritized or actions are recommended.
Contribute to approaches that assess the potential scope and impact of security issues.
Establish evaluation methods to measure model quality, effectiveness, and reliability over time.
Incorporate product usage signals and feedback to guide continuous improvement of ML and AI systems.
Monitor ML and AI systems in production to ensure stability, safety, and consistent behavior.
Partner with Engineering to operationalize models and AI workflows, supporting safe deployment and iteration.
Collaborate with Product to shape AI-driven user experiences, ensuring alignment with customer needs and trust expectations.
Act as a technical leader and thought partner on applied ML and AI across the product area.

Requirements

7–10+ years of experience as a Data Scientist, Applied Scientist, or Machine Learning Engineer, with ownership of production systems.
Experience in security, identity, fraud, or risk modeling domains.
Strong background in statistical modeling, machine learning, and applied decision systems.
Experience designing and shipping ML- or AI-driven product features used by customers.
Experience applying ML or AI to decision-making systems that influence user workflows or automated outcomes.
Comfortability working within the GCP stack, particularly big data services, such as Dataproc (pyspark), Dataflow (Apache Beam), PubSub (Apache Kafka), data lakes (storage, partitioning, searching). Also, experience with SQL, Python, and related Data Science libs (such as scikit-learn, pytorch, GCP integrations, etc).
Experience designing or contributing to agent-like or automated workflows, including reasoning about task decomposition, tool usage, or control flow.
Demonstrated ability to design guardrails and human-in-the-loop mechanisms for automated or AI-assisted actions.
Experience operating ML or AI systems post-launch, including monitoring behavior, iterating based on feedback, and addressing reliability or trust issues.
Familiarity with LLMs and agent-based approaches, with practical awareness of reliability, safety, and evaluation considerations.
Ability to balance automation, explainability, and user trust in customer-facing systems.
Experience partnering closely with Product and Engineering to deliver customer-facing capabilities.
Strong written and verbal communication skills.

Benefits

Base Salary: The annual base salary compensation range in the U.S. for this role is: $250,000 - $300,000 USD. Final offer amounts are determined by factors such as the final candidate’s skills, qualifications, and experience, as well as business considerations and peer compensation.
Stock Options: Our vision is to not just grow as a company but to grow together. By offering stock options, we are inviting you to be an integral part of our journey forward.
Benefits: Generous paid time off, paid company holidays, paid floating holidays, paid parental leave, paid sick time and paid family leave for applicable states, health insurance - medical, dental, and vision with HSA option, LifeWorks Employee Assistance Program, company-provided life insurance, AD&D, STD/LTD and additional supplemental life insurance options, 401(k) and Roth retirement saving accounts, and a monthly wellness benefit reimbursement.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data-driven risk scoringstatistical modelingmachine learningapplied decision systemsML product featuresGCP stackSQLPythonscikit-learnpytorch

Soft Skills

technical leadershipcollaborationcommunicationcustomer alignmenttrust buildingcontinuous improvementproblem-solvingdecision-makingfeedback incorporationexplainability